Data Engineer II

The ideal candidate combines deep hands-on engineering expertise with strong data modeling and solution design skills and can independently lead complex engineering efforts from requirements through production implementation. Advanced Data Engineering • Design, build, and maintain scalable data pipelines and data processing solutions using Databricks, Apache Spark, PySpark, Python, and SQL. • Develop robust ingestion, transformation, and publishing processes supporting batch and, where appropriate, near-real-time data workloads. • Engineer reusable frameworks and patterns that improve development consistency, reliability, maintainability, and speed of delivery. • Design solutions capable of processing large and complex datasets while maintaining appropriate performance and scalability. • Troubleshoot complex data pipeline, transformation, performance, and integration issues. • Optimize Databricks workloads for processing efficiency, reliability, and cost. Databricks & Lakehouse Engineering • Develop enterprise data solutions leveraging Databricks Lakehouse architecture and Delta Lake. • Design and maintain structured data layers that move data from raw ingestion through validated, curated, and business-consumption layers. • Implement scalable transformation patterns using Spark SQL, PySpark, SQL, and Delta Lake. • Apply Databricks engineering capabilities such as Unity Catalog, workflows/jobs, Delta tables, and data governance controls where appropriate. • Support performance tuning, workload optimization, monitoring, and production support for Databricks-based solutions. • Partner with platform and architecture teams to establish repeatable Databricks engineering standards and development patterns. Advanced Data Modeling • Design scalable logical and physical data models supporting enterprise reporting, analytics, and downstream data products. • Develop dimensional models, fact and dimension structures, curated analytical datasets, and reusable semantic-ready data structures. • Translate complex operational source data into intuitive business-oriented models. • Establish appropriate grain, keys, relationships, historical handling, and business rules within analytical data models. • Balance normalization, dimensional modeling, and denormalized analytical structures based on workload and consumption needs. • Partner with BI and analytics teams to optimize models for Power BI and other analytical consumers. Data Quality & Governance • Build automated data validation and reconciliation capabilities into engineering pipelines. • Establish controls for completeness, accuracy, timeliness, uniqueness, and referential integrity. • Implement appropriate monitoring, exception handling, logging, and alerting. • Partner with data governance teams to improve metadata, lineage, documentation, and ownership of enterprise data products. • Ensure engineering solutions comply with applicable security, privacy, and enterprise data-management standards. Technical Leadership • Serve as a senior technical resource for complex data engineering and modeling initiatives. • Lead technical design discussions and recommend appropriate engineering approaches. • Perform code and design reviews and help establish development standards and engineering best practices. • Mentor data engineers and provide guidance on Databricks, Spark, SQL, Python, data modeling, and performance optimization. • Identify opportunities to simplify legacy processes and modernize existing data solutions. • Collaborate effectively across onshore/offshore engineering, analytics, architecture, product, and business teams. Required Qualifications • 7+ years of professional experience in data engineering, data warehousing, business intelligence engineering, or related disciplines. • 3+ years of hands-on experience developing production solutions using Databricks and Apache Spark. • Advanced proficiency in SQL and strong experience with Python/PySpark. • Strong hands-on experience with Delta Lake and modern lakehouse engineering patterns. • Demonstrated experience designing and implementing complex ETL/ELT pipelines. • Strong understanding of dimensional data modeling, including fact tables, dimensions, surrogate keys, slowly changing dimensions, and analytical data structures. • Experience designing solutions for large-volume enterprise datasets. • Demonstrated ability to diagnose and optimize complex SQL, Spark, and data-processing workloads. • Experience implementing data validation, reconciliation, monitoring, and production support processes. • Experience working with source-to-target mappings and translating business requirements into technical data solutions. • Strong analytical, troubleshooting, documentation, and communication skills. • Ability to independently own technical solutions while collaborating within distributed global teams. Preferred Qualifications • Experience with Databricks Unity Catalog and enterprise data governance. • Experience with Databricks workflow orchestration and production deployment patterns. • Experience with cloud data platforms and object storage, particularly Microsoft Azure environments. • Experience supporting Power BI and enterprise semantic/reporting solutions. • Familiarity with CI/CD and automated deployment practices for data engineering. • Experience integrating REST APIs, relational databases, SaaS platforms, file-based sources, and other enterprise data sources. • Experience modernizing legacy ETL or traditional data warehouse workloads into Databricks/lakehouse architecture. • Knowledge of data lineage, metadata management, access controls, and data governance practices. • Databricks certifications are a plus. Leadership & Behavioral Competencies The successful candidate will demonstrate: • Technical ownership: Takes responsibility for solutions from design through production support. • Engineering judgment: Selects pragmatic solutions based on scalability, reliability, maintainability, performance, and cost. • Problem solving: Can independently investigate complex data and platform issues and drive them to resolution. • Quality mindset: Treats data quality, reconciliation, observability, and documentation as core engineering requirements. • Collaboration: Works effectively across business, analytics, architecture, BI, and engineering teams. • Technical leadership: Raises the capabilities of the broader engineering team through mentoring, standards, reusable patterns, and design guidance. • Business orientation: Understands that successful data engineering is measured by the usability and reliability of the resulting data products, not simply by pipeline completion. What Success Looks Like A successful Senior Data Engineer will: • Deliver reliable, scalable, production-ready Databricks data solutions. • Reduce complexity by creating reusable engineering patterns rather than one-off pipelines. • Improve the performance and maintainability of enterprise data workloads. • Produce well-designed data models that simplify downstream analytics and reporting. • Improve data trust through automated validation, reconciliation, monitoring, and documentation. • Provide senior-level technical direction to other engineers while remaining highly hands-on. • Help advance the Enterprise Intelligence organization toward a more standardized, governed, and scalable lakehouse architecture. Required Qualifications • BE with 3-6 years of professional experience in data engineering, data warehousing, business intelligence engineering, or related disciplines. • 3+ years of hands-on experience developing production solutions using Databricks and Apache Spark.